ChatHuggingFace

This will help you get started with langchain_huggingface chat models. For detailed documentation of all ChatHuggingFace features and configurations head to the API reference. For a list of models supported by Hugging Face check out this page.

Overview

Integration details

Integration details

Class	Package	Local	Serializable	JS support	Package downloads	Package latest
ChatHuggingFace	langchain-huggingface	✅	beta	❌

Model features

Tool calling	Structured output	JSON mode	Image input	Audio input	Video input	Token-level streaming	Native async	Token usage	Logprobs
✅	✅	❌	✅	✅	✅	❌	✅	✅	❌

Setup

To access Hugging Face models you'll need to create a Hugging Face account, get an API key, and install the langchain-huggingface integration package.

Credentials

Generate a Hugging Face Access Token and store it as an environment variable: HUGGINGFACEHUB_API_TOKEN.

import getpass
import os

if not os.getenv("HUGGINGFACEHUB_API_TOKEN"):
    os.environ["HUGGINGFACEHUB_API_TOKEN"] = getpass.getpass("Enter your token: ")

Installation

Class	Package	Local	Serializable	JS support	Package downloads	Package latest
ChatHuggingFace	langchain-huggingface	✅	❌	❌

Model features

Tool calling	Structured output	JSON mode	Image input	Audio input	Video input	Token-level streaming	Native async	Token usage	Logprobs
✅	✅	❌	❌	❌	❌	❌	❌	❌	❌

Setup

To access langchain_huggingface models you'll need to create a Hugging Face account, get an API key, and install the langchain-huggingface integration package.

Credentials

You'll need to have a Hugging Face Access Token saved as an environment variable: HUGGINGFACEHUB_API_TOKEN.

import getpass
import os

os.environ["HUGGINGFACEHUB_API_TOKEN"] = getpass.getpass(
    "Enter your Hugging Face API key: "
)

%pip install --upgrade --quiet  langchain-huggingface text-generation transformers google-search-results numexpr langchainhub sentencepiece jinja2 bitsandbytes accelerate

[notice] A new release of pip is available: 24.0 -> 24.1.2
[notice] To update, run: pip install --upgrade pip
Note: you may need to restart the kernel to use updated packages.

Instantiation

You can instantiate a ChatHuggingFace model in two different ways, either from a HuggingFaceEndpoint or from a HuggingFacePipeline.

HuggingFaceEndpoint

from langchain_huggingface import ChatHuggingFace, HuggingFaceEndpoint

llm = HuggingFaceEndpoint(
    repo_id="deepseek-ai/DeepSeek-R1-0528",
    task="text-generation",
    max_new_tokens=512,
    do_sample=False,
    repetition_penalty=1.03,
    provider="auto",  # let Hugging Face choose the best provider for you
)

chat_model = ChatHuggingFace(llm=llm)

The token has not been saved to the git credentials helper. Pass `add_to_git_credential=True` in this function directly or `--add-to-git-credential` if using via `huggingface-cli` if you want to set the git credential as well.
Token is valid (permission: fineGrained).
Your token has been saved to /Users/isaachershenson/.cache/huggingface/token
Login successful

Now let's take advantage of Inference Providers to run the model on specific third-party providers

llm = HuggingFaceEndpoint(
    repo_id="deepseek-ai/DeepSeek-R1-0528",
    task="text-generation",
    provider="hyperbolic",  # set your provider here
    # provider="nebius",
    # provider="together",
)

chat_model = ChatHuggingFace(llm=llm)

HuggingFacePipeline

from langchain_huggingface import ChatHuggingFace, HuggingFacePipeline

llm = HuggingFacePipeline.from_model_id(
    model_id="HuggingFaceH4/zephyr-7b-beta",
    task="text-generation",
    pipeline_kwargs=dict(
        max_new_tokens=512,
        do_sample=False,
        repetition_penalty=1.03,
    ),
)

chat_model = ChatHuggingFace(llm=llm)

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Instatiating with Quantization

To run a quantized version of your model, you can specify a bitsandbytes quantization config as follows:

from transformers import BitsAndBytesConfig

quantization_config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_quant_type="nf4",
    bnb_4bit_compute_dtype="float16",
    bnb_4bit_use_double_quant=True,
)

and pass it to the HuggingFacePipeline as a part of its model_kwargs:

llm = HuggingFacePipeline.from_model_id(
    model_id="HuggingFaceH4/zephyr-7b-beta",
    task="text-generation",
    pipeline_kwargs=dict(
        max_new_tokens=512,
        do_sample=False,
        repetition_penalty=1.03,
        return_full_text=False,
    ),
    model_kwargs={"quantization_config": quantization_config},
)

chat_model = ChatHuggingFace(llm=llm)

Invocation

from langchain_core.messages import (
    HumanMessage,
    SystemMessage,
)

messages = [
    SystemMessage(content="You're a helpful assistant"),
    HumanMessage(
        content="What happens when an unstoppable force meets an immovable object?"
    ),
]

ai_msg = chat_model.invoke(messages)

API Reference:HumanMessage | SystemMessage

print(ai_msg.content)

According to the popular phrase and hypothetical scenario, when an unstoppable force meets an immovable object, a paradoxical situation arises as both forces are seemingly contradictory. On one hand, an unstoppable force is an entity that cannot be stopped or prevented from moving forward, while on the other hand, an immovable object is something that cannot be moved or displaced from its position. 

In this scenario, it is un

API reference

For detailed documentation of all ChatHuggingFace features and configurations head to the API reference: https://python.langchain.com/api_reference/huggingface/chat_models/langchain_huggingface.chat_models.huggingface.ChatHuggingFace.html

API reference

For detailed documentation of all ChatHuggingFace features and configurations head to the API reference: https://python.langchain.com/api_reference/huggingface/chat_models/langchain_huggingface.chat_models.huggingface.ChatHuggingFace.html

Related

• Chat model conceptual guide
• Chat model how-to guides